Why this topic?

Cancer in the 1960’s – hushed tones

Cancer in the 1990’s – we are finding cures!

Cancer in 2015 – we can be very hopeful, but

realize it may be an ongoing malady

For example, we are more aware of late

after effects with pediatric patients,

and now with adults

A snapshot In time

Casefinding

Follow-up (80% reference yr

90% 5 yrs)

Abstracting (90% in 6 months) Reporting

“The abstract is the basis of registry functions” NCRA Informational Abstracts, March 19, 2015

To what extent can we automate the process of

completing an abstract?

Will “auto-abstracting” enhance the quality and

timeliness of your cancer reporting?

We will examine:

Success with automation to date

• Cancer casefinding from pathology reports

• Cancer casefinding from head and neck diagnostic imaging

reports

• Cancer record linkage

• Extraction of synoptic data from text reports

• Our understanding of the pathophysiology of neoplasia

• Investigations used to diagnose cancer

• Classification of malignancies

• New cancer therapies

• Evolving reporting standards

• Where diagnosis and treatment occur

• All of these are assisted by advances in information technology

Reviewing Our Changing Environment

Harrison’s Principles of Internal Medicine, 1970

Classification of

Leukemias, 1970

Harrison’s Principles of Internal Medicine, 2005

2018?

Classification of

Leukemias

and Lymphoid

Malignacies, 2005

2015 1995

Diagnosing Lymphoma

• Radiograph

• CT Scan

• Biopsy

• Blood Work

• Radiograph

• CT Scan

• Biopsy

• Blood Work

• CD 30 Expression

• Anaplastic Lymphoma

Kinase (ALK)

• PET Scan (FDG Avidity) Source(s): NEJM Vol 333, No 12, p 784 Sep 21, 1995 | NEJM Vol 372, No 7, p 650 Feb 12, 20152009-2010

Increasing Complexity & Volume of Cancer Data

Genetic Abnormalities in Chronic Lymphocytic Leukemia

Sequence the DNA of 91 patients

To examine the spectrum of mutations

in this disease All patients

are clinically the same but

genetically different

NEJM Vol 365, No 26

And may respond to different

treatments!

• Personalized / Precision Medicine

• Disease detection / screening

• Determining Extent of Disease

• Detection of recurrence

• Outcome and its time course

Cancer is a progressive disease, so very accurate

information is necessary for

• Integration of registry data with screening programs

• Extraction of staging information

• How to continue to gather data

• How to identify and track recurrence over the time course of

cancer

• Again, the current abstract format does not allow for some

of these data elements

So an automated design must incorporate

Plotting the time course of events:

• Each child’s continuum of care is a series of events

• Each event is annotated with socio-economic details

• Quality of Life considerations

2009

1st Diagnosis

Treatment

2010

Active Follow-up

2014

Recurrence

Treatment

2015

Active Follow-up

Active Cancer Registry (Pediatric)

• Who has access to the information – full audit trail required

• HIPAA / HITECH security and privacy requirements

• Financial penalties for data breaches

• Who is custodian of stored records over time, perhaps past

the legal retention period

Regulatory and Security Concerns

Source(s): The State of Cancer Care in America, 2014: A Report by the American Society of Clinical Oncology Journal of Oncology Practice JOP.2014.001386 Published online March 10, 2014

Most Oncology Data are now Electronic

2008

2012-2013

EMR Adoption in 4,300 Oncology Practices

Lab Systems

Lab Systems

EMR EMR

Dx Imaging

Genetic Biomarkers

Pharmaceutical

Surgical Radiotherapy

Death Registries

Increasingly all in Electronic Format

Cancer Care Information Sources

Getting the documents out of current

Electronic Medical Record systems

1.

Getting the data out of the documents 2.

Keeping updated with the different

vocabularies and standards

3.

Challenges with Automating Sources

Eliminating redundancy 1.

Assessing data reliability and quality 2.

Determining how the data can be

processed automatically

3.

Challenges with Automating Data

To what extent can we automate the

process of completing an abstract?

The Initial Question

Use Artificial Intelligence (A.I.)

Our Approach

Relegate the repetitive to

Artificial Intelligence systems,

Focus registry staff on more complex tasks

Our Practical and Economical Approach

The capability of a computer to emulate

intelligent human behavior

Artificial Intelligence

Most successful in clearly demarcated domains

For example, it works well in protein

electrophoresis interpretation, but not in

general internal medicine

General Purpose

Limited Accuracy

Specific Purpose

Very high Accuracy

Where to Apply A.I.

Hard Easy

Tic-Tac-Toe

Checkers

Chess

GO

Image Processing

Voice Recognition

Locomotion

Composition Natural Language Processing

Artificial Intelligence

• Your smart phone assistant can sort and present the

restaurants within one mile…

• THEN YOU DECIDE

• Artificial Intelligence can perform the basic data gathering

tasks for an abstract…

• THEN YOU APPLY YOUR EXPERTISE

A.I. on a Practical Level

Artificial Intelligence

Search Optimization

Neural Networks

Semantic Maps

Bayesian Networks

Quantum Computing

Speech Recognition

Computer Vision

Sub-Disciplines

Inference Systems

Expert Systems

Natural Language Processing

Markov Random Fields

Pattern Matching

State Machines

Inference Systems

Expert Systems

Natural Language Processing

Search Optimization

Neural Networks

Semantic Maps

Bayesian Networks

Quantum Computing

Speech Recognition

Computer Vision

Markov Random Fields

Pattern Matching

State Machines

AIM’s Focus

“Time flies like an arrow”

“Fruit flies like an apple”

“All the lymph nodes are negative”

“Diagnosis: ALL”

Natural Language Processing can distinguish

“Time flies like an arrow” from “Fruit flies like an apple”

and

“ALL” from “all”

• Words in Context

• Modifiers, especially negation

• Regular Expressions

• Grammar / Formatting / Punctuation

Knowledge Base Knowledge

Base

Functional Architecture

Knowledge Base

Rule Base

Primary Search

Report Decomposition

Concept Isolation

Negation Detection

Concept-Value Synthesis

Pathology Report

Final Variable-Value Pairs

Exclusion Process

Refinement

A.I. Engine

Data processed automatically, confidence level 95%+

1.

Data processed automatically, then reviewed by a CTR due to a lower confidence level

2.

Incomplete or contradictory data that must be adjudicated by a CTR

3.

Confidence Level of the Data

We have already successfully automated:

• Cancer casefinding from pathology reports

• Cancer casefinding from head and neck diagnostic imaging

reports

• Cancer record linkage

• Extraction of synoptic data from text reports

• Pathology

• Diagnostic Imaging

• Genome Sequencing

• Biomarkers

• Surgery

• Chemotherapy

• Radiotherapy

• Follow-up

• Recurrence

• And ?

Next logical step: Our current project is to put it all together to

auto-populate the abstract!

With CDA - Clinical Data Architecture – we can

consolidate documents of differing formats

Maintained by HL7 and is a key component of government

health initiatives

A document standard is now available

Cancer Abstract Data Inputs

radiology

radiotherapy

chemotherapy

1999 2015

50

100 Sensitivity

Specificity

99.7

98.9

Performance Improvement Over Time

The Experience Curve (E-Path)

SENSITIVITY receiving the data that you want [few false negatives]

SPECIFICITY excluding the data that you don’t want [few false positives]

Standardization

Consistency

Auto-Populated Fields

Completeness

Accuracy

Low Cost

Benefits of Auto-Abstracting

How far have we automated the process of completing an abstract?

Will “auto-abstracting” enhance the quality and timeliness of your cancer reporting?

Yes!

To a very significant extent!

Your registry remains the basis of a comprehensive

cancer record based on a national standard within an

existing legal framework!

(not that expensive new EMR system in the hospital)

Auto-abstracting will help to ensure that:

For your cancer registry, and the cancer registry community, this will raise your profile and the level of respect that you receive in your hospital!